AutoEncoder是包含一个压缩和解压缩的过程，属于一种无监督学习的降维技术。

神经网络接受大量信息，有时候接受的数据达到上千万，可以通过压缩

提取原图片最具有代表性的信息，压缩输入的信息量，在将缩减后的数据放入神经网络中学习，如此学习起来变得轻松了

自编码在这个时候使用，可以将自编码归为无监督学习，类似于PCA，自编码可以为属性降维

手写体识别代码AutoEncoder

from __future__ import division, print_function, absolute_import

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

# Import MNIST data

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('MNIST_data', one_hot=False)

# Visualize decoder setting

# Parameters

learning_rate = 0.01

training_epochs = 5

batch_size = 256

display_step = 1

examples_to_show = 10

# Network Parameters

n_input = 784  # MNIST data input (img shape: 28*28)

# tf Graph input (only pictures)

X = tf.placeholder("float", [None, n_input])

# hidden layer settings

n_hidden_1 = 256 # 1st layer num features

n_hidden_2 = 128 # 2nd layer num features

weights = {

    'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),

    'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),

    'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),

    'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])),

}

biases = {

    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),

    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),

    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),

    'decoder_b2': tf.Variable(tf.random_normal([n_input])),

}

# Building the encoder

def encoder(x):

    # Encoder Hidden layer with sigmoid activation #1

    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),

                                   biases['encoder_b1']))

    # Decoder Hidden layer with sigmoid activation #2

    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),

                                   biases['encoder_b2']))

    return layer_2

# Building the decoder

def decoder(x):

    # Encoder Hidden layer with sigmoid activation #1

    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),

                                   biases['decoder_b1']))

    # Decoder Hidden layer with sigmoid activation #2

    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),

                                   biases['decoder_b2']))

    return layer_2

"""

# Visualize encoder setting

# Parameters

learning_rate = 0.01    # 0.01 this learning rate will be better! Tested

training_epochs = 10

batch_size = 256

display_step = 1

# Network Parameters

n_input = 784  # MNIST data input (img shape: 28*28)

# tf Graph input (only pictures)

X = tf.placeholder("float", [None, n_input])

# hidden layer settings

n_hidden_1 = 128

n_hidden_2 = 64

n_hidden_3 = 10

n_hidden_4 = 2  #2D show

weights = {

    'encoder_h1': tf.Variable(tf.truncated_normal([n_input, n_hidden_1],)),

    'encoder_h2': tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2],)),

    'encoder_h3': tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_3],)),

    'encoder_h4': tf.Variable(tf.truncated_normal([n_hidden_3, n_hidden_4],)),

    'decoder_h1': tf.Variable(tf.truncated_normal([n_hidden_4, n_hidden_3],)),

    'decoder_h2': tf.Variable(tf.truncated_normal([n_hidden_3, n_hidden_2],)),

    'decoder_h3': tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_1],)),

    'decoder_h4': tf.Variable(tf.truncated_normal([n_hidden_1, n_input],)),

}

biases = {

    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),

    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),

    'encoder_b3': tf.Variable(tf.random_normal([n_hidden_3])),

    'encoder_b4': tf.Variable(tf.random_normal([n_hidden_4])),

    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_3])),

    'decoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),

    'decoder_b3': tf.Variable(tf.random_normal([n_hidden_1])),

    'decoder_b4': tf.Variable(tf.random_normal([n_input])),

}

def encoder(x):

    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),

                                   biases['encoder_b1']))

    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),

                                   biases['encoder_b2']))

    layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['encoder_h3']),

                                   biases['encoder_b3']))

    layer_4 = tf.add(tf.matmul(layer_3, weights['encoder_h4']),

                                    biases['encoder_b4'])

    return layer_4

def decoder(x):

    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),

                                   biases['decoder_b1']))

    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),

                                   biases['decoder_b2']))

    layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['decoder_h3']),

                                biases['decoder_b3']))

    layer_4 = tf.nn.sigmoid(tf.add(tf.matmul(layer_3, weights['decoder_h4']),

                                biases['decoder_b4']))

    return layer_4

"""

# Construct model

encoder_op = encoder(X)

decoder_op = decoder(encoder_op)

# Prediction

y_pred = decoder_op

# Targets (Labels) are the input data.

y_true = X

# Define loss and optimizer, minimize the squared error

cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))

optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)

# Launch the graph

with tf.Session() as sess:

    # tf.initialize_all_variables() no long valid from

    # 2017-03-02 if using tensorflow >= 0.12

    if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:

        init = tf.initialize_all_variables()

    else:

        init = tf.global_variables_initializer()

    sess.run(init)

    total_batch = int(mnist.train.num_examples/batch_size)

    # Training cycle

    for epoch in range(training_epochs):

        # Loop over all batches

        for i in range(total_batch):

            batch_xs, batch_ys = mnist.train.next_batch(batch_size)  # max(x) = 1, min(x) = 0

            # Run optimization op (backprop) and cost op (to get loss value)

            _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})

        # Display logs per epoch step

        if epoch % display_step == 0:

            print("Epoch:", '%04d' % (epoch+1),

                  "cost=", "{:.9f}".format(c))

    print("Optimization Finished!")

    # # Applying encode and decode over test set

    encode_decode = sess.run(

        y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})

    # Compare original images with their reconstructions

    f, a = plt.subplots(2, 10, figsize=(10, 2))

    for i in range(examples_to_show):

        a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))

        a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))

    plt.show()

    # encoder_result = sess.run(encoder_op, feed_dict={X: mnist.test.images})

    # plt.scatter(encoder_result[:, 0], encoder_result[:, 1], c=mnist.test.labels)

    # plt.colorbar()

    # plt.show()

利用AutoEncoder进行类似于PCA的降维

代码：

from __future__ import division, print_function, absolute_import

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

# Import MNIST data

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('MNIST_data', one_hot=False)

"""

# Visualize decoder setting

# Parameters

learning_rate = 0.01

training_epochs = 5

batch_size = 256

display_step = 1

examples_to_show = 10

# Network Parameters

n_input = 784  # MNIST data input (img shape: 28*28)

# tf Graph input (only pictures)

X = tf.placeholder("float", [None, n_input])

# hidden layer settings

n_hidden_1 = 256 # 1st layer num features

n_hidden_2 = 128 # 2nd layer num features

weights = {

    'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),

    'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),

    'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),

    'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])),

}

biases = {

    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),

    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),

    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),

    'decoder_b2': tf.Variable(tf.random_normal([n_input])),

}

# Building the encoder

def encoder(x):

    # Encoder Hidden layer with sigmoid activation #1

    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),

                                   biases['encoder_b1']))

    # Decoder Hidden layer with sigmoid activation #2

    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),

                                   biases['encoder_b2']))

    return layer_2

# Building the decoder

def decoder(x):

    # Encoder Hidden layer with sigmoid activation #1

    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),

                                   biases['decoder_b1']))

    # Decoder Hidden layer with sigmoid activation #2

    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),

                                   biases['decoder_b2']))

    return layer_2

"""

# Visualize encoder setting

# Parameters

learning_rate = 0.01    # 0.01 this learning rate will be better! Tested

training_epochs = 10

batch_size = 256

display_step = 1

# Network Parameters

n_input = 784  # MNIST data input (img shape: 28*28)

# tf Graph input (only pictures)

X = tf.placeholder("float", [None, n_input])

# hidden layer settings

n_hidden_1 = 128

n_hidden_2 = 64

n_hidden_3 = 10

n_hidden_4 = 2  #2D show

weights = {

    'encoder_h1': tf.Variable(tf.truncated_normal([n_input, n_hidden_1],)),

    'encoder_h2': tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2],)),

    'encoder_h3': tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_3],)),

    'encoder_h4': tf.Variable(tf.truncated_normal([n_hidden_3, n_hidden_4],)),

    'decoder_h1': tf.Variable(tf.truncated_normal([n_hidden_4, n_hidden_3],)),

    'decoder_h2': tf.Variable(tf.truncated_normal([n_hidden_3, n_hidden_2],)),

    'decoder_h3': tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_1],)),

    'decoder_h4': tf.Variable(tf.truncated_normal([n_hidden_1, n_input],)),

}

biases = {

    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),

    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),

    'encoder_b3': tf.Variable(tf.random_normal([n_hidden_3])),

    'encoder_b4': tf.Variable(tf.random_normal([n_hidden_4])),

    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_3])),

    'decoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),

    'decoder_b3': tf.Variable(tf.random_normal([n_hidden_1])),

    'decoder_b4': tf.Variable(tf.random_normal([n_input])),

}

def encoder(x):

    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),

                                   biases['encoder_b1']))

    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),

                                   biases['encoder_b2']))

    layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['encoder_h3']),

                                   biases['encoder_b3']))

    layer_4 = tf.add(tf.matmul(layer_3, weights['encoder_h4']),

                                    biases['encoder_b4'])

    return layer_4

def decoder(x):

    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),

                                   biases['decoder_b1']))

    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),

                                   biases['decoder_b2']))

    layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['decoder_h3']),

                                biases['decoder_b3']))

    layer_4 = tf.nn.sigmoid(tf.add(tf.matmul(layer_3, weights['decoder_h4']),

                                biases['decoder_b4']))

    return layer_4

# Construct model

encoder_op = encoder(X)

decoder_op = decoder(encoder_op)

# Prediction

y_pred = decoder_op

# Targets (Labels) are the input data.

y_true = X

# Define loss and optimizer, minimize the squared error

cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))

optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)

# Launch the graph

with tf.Session() as sess:

    # tf.initialize_all_variables() no long valid from

    # 2017-03-02 if using tensorflow >= 0.12

    if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:

        init = tf.initialize_all_variables()

    else:

        init = tf.global_variables_initializer()

    sess.run(init)

    total_batch = int(mnist.train.num_examples/batch_size)

    # Training cycle

    for epoch in range(training_epochs):

        # Loop over all batches

        for i in range(total_batch):

            batch_xs, batch_ys = mnist.train.next_batch(batch_size)  # max(x) = 1, min(x) = 0

            # Run optimization op (backprop) and cost op (to get loss value)

            _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})

        # Display logs per epoch step

        if epoch % display_step == 0:

            print("Epoch:", '%04d' % (epoch+1),

                  "cost=", "{:.9f}".format(c))

    print("Optimization Finished!")

#     # # Applying encode and decode over test set

#     encode_decode = sess.run(

#         y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})

#     # Compare original images with their reconstructions

#     f, a = plt.subplots(2, 10, figsize=(10, 2))

#     for i in range(examples_to_show):

#         a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))

#         a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))

#     plt.show()

    encoder_result = sess.run(encoder_op, feed_dict={X: mnist.test.images})

    plt.scatter(encoder_result[:, 0], encoder_result[:, 1], c=mnist.test.labels)

    plt.colorbar()

    plt.show()

显示如下：

2.3AutoEncoder的更多相关文章

随机推荐

  1. Eclipse------新建文件时没有JSP File解决方法

    1.为没有web选项的eclipse添加web and JavaEE插件 .在Eclipse中菜单help选项中选择install new software选项 .在work with 栏中输入 Ju ...

  2. flexbox子盒子align-self属性

    <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8&quo ...

  3. Django SimpleCMDB WSGI

    一.WSGI 介绍 (1) 在前面的学习中,我们是通过 python manage.py runserver 0.0.0.0:8000 来启动并访问开发服务器的:(2) 但在实际中我们是通过直接访问 ...

  4. 带有ZLIB_LIBRARY_DEBUG的FindZLIB.cmake文件

    CMake自带的FindZLIB.cmake只有ZLIB_LIBRARY,而没有ZLIB_LIBRARY_DEBUG 将下面的代码保存成FindZLIB.cmake,替换掉D:\Program Fil ...

  5. CopyTransform

    // TransformCopier.cs v 1.1 // homepage: http://wiki.unity3d.com/index.php/CopyTransform using Unity ...

  6. [C] 如何使用头文件 .h 编译 C 源码

    在 C 语言中,头文件或包含文件通常是一个源代码文件,程序员使用编译器指令将头文件包含进其他源文件的开始(或头部),由编译器在处理另一个源文件时自动包含进来. 一个头文件一般包含类.子程序.变量和其他 ...

  7. update select 多字段

    update Countrys set ( Abbreviation_cn, Abbreviation_en, Two_code,Three_code, Number_code)= (select [ ...

  8. Ubuntu Releases 版本下载站

    http://releases.ubuntu.com/

  9. 九度OJ小结

    1. 高精度问题 可参考题目 题目1137:浮点数加法   http://ac.jobdu.com/problem.php?pid=1137 对于高精度问题可以考虑使用结构体.上述为浮点数加法,因此该 ...

  10. [原]sublime Text2

    sublime Text2 升级到 2.0.2 build 2221 64位 的破破解 sublime Text2 download website 链接: http://pan.baidu.com/ ...